Just In Time! Assessment of Internal Medicine Resident Point of Care Ultrasound (POCUS) Attitudes and Behaviors After Spaced Intervention at Two Residency Programs
Bibliographic record
Abstract
Point of care ultrasound (POCUS) is a complex psychomotor skill that requires scaffolded support for skill acquisition. However, the effect of spaced curricular elements on learner POCUS behaviors are not clearly understood. Using a multi-site observational cross-sectional survey study, we measured resident baseline POCUS use, behaviors, and attitudes and then implemented POCUS workflow and just-in-time POCUS curricula during internal medicine resident ward rotations and assessed changes. Self-reported personal and team POCUS use and documentation habits all improved between baseline and the just-in-time teaching. Personal POCUS use correlated with team POCUS use (ρ=0.431; p<0.001) and co-resident POCUS use (ρ=0.242; p=0.035). Attending POCUS use correlated with team POCUS use (ρ=0.523; p< 0.001), but not personal use. Overall, we found moderate, but statistically significant, improvements in reported resident and team performance of POCUS and documentation habits, suggesting that just-in-time interventions may promote POCUS use. Co-learning also appears to be a key influencer for POCUS use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".